QCast Episode 69: SDTM Mapping Automation Tool

SDTM mapping automation can reduce the manual work involved in converting clinical trial data into standardised datasets. However, differences in source data structures, vendor conventions, and study requirements mean that mapping still involves technical interpretation. In this QCast episode, co-hosts Jullia and Tom discuss how automation tools use approved mapping rules, metadata, and AI-assisted recommendations to support SDTM development, and where experienced programmers remain essential.

Even relatively straightforward data can present mapping challenges. Adverse event dates may be collected in different formats, laboratory data may use vendor-specific terminology, and protocol amendments can introduce changes to existing derivations. Managing these differences consistently helps teams reduce repeated programming work while maintaining the accuracy of SDTM datasets and their supporting documentation.

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Key Takeaways

How metadata supports SDTM mapping automation

Metadata defines how source variables should populate SDTM datasets, including variable attributes, controlled terminology, and derivation rules. Storing approved mappings in a central repository allows programmers to reuse existing components and generate specifications or SAS code without recreating established logic. When study requirements change, affected mappings can be identified, updated, and checked against the relevant standards.

Where AI can assist with mapping decisions

Machine learning can help identify potential SDTM mappings by examining variable names, labels, data types, and previously accepted mappings. However, a suggested mapping may be incorrect if the source variable has a different meaning in the study context. Large language models can also assist with specification development and code generation when provided with approved, structured mapping information, although their outputs still require technical review.

Why automated validation and independent QC are both necessary

Automated validation can identify structural problems, incorrect variable attributes, and issues with controlled terminology. It may not detect an incorrect interpretation already included in the mapping specifications. Independent QC provides an additional check against the original source data, including whether laboratory unit conversions or study-specific derivations have been applied correctly. Maintaining governed mapping metadata also supports downstream documentation such as Define-XML and the Study Data Reviewer's Guide.

Full Transcript

Episode 69: How an SDTM Mapping Automation Tool Supports Data Standardisation

Jullia

Welcome to QCast, the show where biometric expertise meets data-driven dialogue. I’m Jullia.

Tom

I’m Tom, and in each episode, we dive into the methodologies, case studies, regulatory shifts, and industry trends shaping modern drug development.

Jullia

Whether you’re in biotech, pharma or life sciences, we’re here to bring you practical insights straight from a leading biometrics CRO. Let’s get started.

Tom

Clinical trials nowadays already collect most of their data electronically. You might assume that makes standardisation fairly straightforward. So why is SDTM mapping still such a substantial task?

Jullia

Well, collecting data electronically doesn't necessarily mean it's collected consistently. Every study can have different case report forms, laboratory vendors, coding conventions, and data structures.

SDTM, or the Study Data Tabulation Model, provides a standard way of organising that information using CDISC standards. But getting from the original source data to those standardised datasets involves quite a lot of interpretation and programming.

Tom

And I suppose having a common standard doesn't automatically tell you how every source variable should be mapped?

Jullia

Exactly. Take adverse event data, for example. One study might capture an event's start date in a single field, while another collects the day, month, and year separately. See, the underlying information is similar, but the transformation needed to populate the SDTM variable is different.

Programmers also need to consider controlled terminology, which means using agreed values and codes to represent information consistently.

Tom

That sounds manageable for individual variables. Does the difficulty increase when you're combining data from external providers?

Jullia

Quite significantly. Think about laboratory results arriving from an external vendor. The laboratory might use different test codes, variable names, or measurement units from those used elsewhere in the study.

Before that information can be incorporated into SDTM, the team needs to establish how it corresponds to the required standard.

Tom

Which brings us to automation. Where can a mapping tool actually remove some of that manual work?

Jullia

Much of the opportunity comes from activities that follow established rules.

An automation tool can help generate mapping specifications, apply controlled terminology, produce standard derivations, and create draft SAS programming code. Depending on how it's configured, it may also convert source data into draft SDTM datasets and perform automated checks.

Instead of programmers repeatedly writing similar mappings, the tool can use approved rules and specifications to perform those routine activities.

Tom

How does the tool retain those decisions so programmers aren't starting again with every study?

Jullia

So that's where metadata becomes particularly useful.

Metadata describes how source information should populate SDTM variables. It can include variable attributes, origins, controlled terminology, and derivation rules.

When those details are maintained in a structured repository, programmers can reuse previously approved mapping components instead of recreating them.

And because the mapping rules are documented separately from the programming code, it's easier to understand how a particular dataset was produced.

Tom

Would that repository also contain the standards being applied? I'm thinking about sponsors who have their own conventions alongside CDISC requirements.

Jullia

It should. Different sponsors may have additional standards, and studies can use different versions of the SDTM Implementation Guide or controlled terminology. Recording those versions helps teams establish which rules apply to a particular study.

It also means that when something changes, programmers can identify which mappings may be affected rather than assuming that every existing component is still appropriate.

Tom

Suppose a protocol amendment introduces an additional assessment visit. Would the team need to rebuild the affected programming?

Jullia

Not necessarily. If the mapping process is metadata-driven, some changes can be made through the specifications rather than rewriting large sections of code.

For example, the revised visit information might require updates to the mapping rules or derivation logic. Once those changes have been approved, the affected code and datasets can be regenerated and checked.

Of course, an amendment may introduce more complex changes. Automation helps with the routine updates, but the team still needs to assess the consequences of the amendment.

Tom

Where do AI and machine learning enter the process?

Jullia

They can help identify likely mappings when a direct rule isn't already available.

A machine learning model might examine a source variable's name, label, data type, and observed values. It can also compare that information with mappings previously accepted by programmers.

From there, it can recommend an SDTM domain or variable, potentially with a confidence score and evidence supporting the recommendation.

Tom

Could a high confidence score give teams too much reassurance? Especially if the source data looks familiar but means something different?

Jullia

It’s possible. Like, imagine a source field labelled as a treatment date. Depending on the study, that could refer to planned treatment, actual administration, or something else entirely.

A model might recognise the wording and suggest a familiar mapping, but the protocol and collection context determine whether that suggestion is correct.

That's why low-confidence or conflicting recommendations need closer attention, and even high-confidence mappings should follow an appropriate review process.

Tom

And what about large language models? We're seeing them used for coding tasks more generally. Could they generate the mapping specifications and SAS programs?

Jullia

They can assist with both, although how they're used makes a considerable difference.

If a language model receives approved mapping information in a defined, machine-readable structure, it can use those instructions to draft specifications or transformation code.

That's more controlled than asking it to interpret an entire collection of study documents and decide everything independently.

Any generated code still needs to be reviewed, version-controlled, and checked against the approved specifications before it's used to produce SDTM datasets.

Tom

Let's say the code runs successfully and the resulting datasets pass the automated validation checks. How much confidence should the team take from that?

Jullia

Passing validation is useful, but it doesn't automatically establish that every mapping decision was correct.

Automated checks can identify problems with dataset structure, variable attributes, controlled terminology, and relationships between datasets. They can also compare the outputs against the approved specifications.

What they may not identify is an incorrect interpretation that was already embedded in those specifications.

Tom

That's quite an important distinction. How does independent quality control address something that automated validation might miss?

Jullia

Independent QC examines whether the programmed output accurately reflects the approved specifications and the source data.

For example, a laboratory result might appear in the correct SDTM domain with valid variable attributes, but the reviewer still needs to establish whether the appropriate unit conversion or test mapping was applied.

Experienced programmers also assess unusual derivations, protocol-specific requirements, and discrepancies involving external data.

Tom

We've concentrated on producing the SDTM datasets themselves. Does having those mappings stored centrally also make the later submission work easier?

Jullia

It can, particularly because several downstream deliverables depend on information established during mapping.

See, the same governed metadata can support annotated case report forms, Define-XML documentation, and information included in the Study Data Reviewer's Guide.

It also supports the handover to ADaM programming, where SDTM datasets are used to develop analysis-ready data.

Those deliverables each have their own requirements and review processes, but maintaining the links between them reduces repeated manual work and helps reviewers trace information back to its source.

Tom

And I imagine the benefits will vary between a relatively conventional study and one with a more complicated design?

Jullia

Of course. Like, a study with familiar CRF structures and standard mappings may offer more opportunities for automation. But a complex trial involving multiple treatment periods, adaptive visit schedules, or unusual external data may require more manual input.

Tom

Before we finish, what would you want clinical development teams to take away when they're evaluating SDTM mapping automation?

Jullia

First, the quality of the underlying metadata determines how effectively mapping rules can be reused. Poorly defined specifications won't become more reliable simply because they're automated.

Second, automation can make SDTM development more consistent by connecting mapping decisions with code generation and validation. And finally, expert review remains essential.

So for teams dealing with repeated mapping work across several studies, that could make a meaningful difference to how they manage their programming resources.

With that, we’ve come to the end of today’s episode on How an SDTM Mapping Automation Tool Supports Data Standardisation. If you found this discussion useful, don’t forget to subscribe to QCast so you never miss an episode and share it with a colleague. And if you’d like to learn more about how Quanticate supports data-driven solutions in clinical trials, head to our website or get in touch.

Tom

Thanks for tuning in, and we’ll see you in the next episode.

About QCast

QCast by Quanticate is the podcast for biotech, pharma, and life science leaders looking to deepen their understanding of biometrics and modern drug development. Join co-hosts Tom and Jullia as they explore methodologies, case studies, regulatory shifts, and industry trends shaping the future of clinical research. Where biometric expertise meets data-driven dialogue, QCast delivers practical insights and thought leadership to inform your next breakthrough.

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